This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human–robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.

This paper focuses on the trajectory tracking control problem for an articulated unmanned ground vehicle. We propose and compare two approaches in terms of performance and computational complexity. The first uses a nonlinear mathematical model derived from first principles and combines a nonlinear model predictive controller (NMPC) with a nonlinear moving horizon estimator (NMHE) to produce a control strategy. The second is based on an input-state linearization (ISL) of the original model followed by linear model predictive control (LMPC). A fast real-time iteration scheme is proposed, implemented for the NMHE-NMPC framework and benchmarked against the ISL-LMPC framework, which is a traditional and cheap method. The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications. Moreover, the ISL-LMPC produces results of a quality comparable to the NMHE-NMPC framework at a significantly reduced computational cost.

Green infrastructure is a unique combination of economic, social, and environmental goals and benefits that requires an adaptable framework for planning, implementing, and evaluating. In this study, we propose an experimental framework for policy, implementation, and subsequent evaluation of green stormwater infrastructure within the context of sociotechnical systems and urban experimentation. Sociotechnical systems describe the interaction of complex systems with quantitative and qualitative impacts. Urban experimentation—traditionally referencing climate change programs and their impacts—is a process of evaluating city programs as if in a laboratory setting with hypotheses and evaluated results. We combine these two concepts into a singular framework creating a policy feedback cycle (PFC) for green infrastructure to evaluate municipal green infrastructure plans as an experimental process within the context of a sociotechnical system. After proposing and discussing the PFC, we utilize the tool to research and evaluate the green infrastructure programs of 27 municipalities across the United States. Results indicate that green infrastructure plans should incorporate community involvement and communication, evaluation based on project motivation, and an iterative process for knowledge production. We suggest knowledge brokers as a key resource in connecting the evaluation stage of the feedback cycle to the policy phase. We identify three important needs for green infrastructure experimentation: (i) a fluid definition of green infrastructure in policy; (ii) maintenance and evaluation components of a green infrastructure plan; and (iii) communication of the plan to the community.

This article reviews the human–machine interaction (HMI) technologies used for telemanipulation by small unmanned systems (SUS) with remote manipulators. SUS, including land, air, and sea vehicles, can perform a wide range of reconnaissance and manipulation tasks with varying levels of autonomy. SUS operations involving physical interactions with the environment require some level of operator involvement, ranging from direct control to goal-oriented supervision. Telemanipulation remains a challenging task for all levels of human interaction because the operator and the vehicle are not colocated, and operators require HMI technologies that facilitate manipulation from a remote location. This article surveys the human operator interfacing for over 70 teleoperated systems, summarizes the effects of physical and visual interface factors on user performance, and discusses these findings in the context of telemanipulating SUS. This article is of importance to SUS researchers and practitioners who will directly benefit from HMI implementations that improve telemanipulation performance.

This article reviews the different techniques used to monitor the respiration and sounds of livestock. Livestock respiration is commonly assessed visually by observing abdomen fluctuation; however, the traditional methods are time consuming, subjective, being therefore impractical for large-scale operations and must rely on automation. Contact and non-contact technologies are used to automatically monitor respiration rate; contact technologies (e.g., accelerometers, pressure sensors, and thermistors) utilize sensors that are physically mounted on livestock while non-contact technologies (e.g., computer vision, thermography, and sound analysis) enable a non-invasive method of monitoring respiration. This work summarizes the advantages and disadvantages of contact and non-contact technologies and discusses the emerging role of non-contact sensors in automating monitoring for large-scale farming operations. This work is the first in-depth examination of automated monitoring technologies for livestock respiratory diseases; the findings and recommendations are important for livestock researchers and practitioners who can gain a better understanding of these different technologies, especially emerging non-contact sensing.

This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as ``tanks'') at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed.